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Semantic Web Technologies as a Framework for Clinical Informatics

2.
Me <ul><li>I work in the Heart and Vascular Institute at the Cleveland Clinic </li></ul><ul><li>We store and query patient populations as an RDF dataset </li></ul><ul><li>Ph.D student at Case Western Reserve University </li></ul><ul><li>Researching medical informatics methodology </li></ul>

5.
Bioinformatics <ul><li>Bioinformatics </li></ul><ul><ul><li>Discipline of gathering, analyzing, and representing the structure / function of genes and proteins and correlating these to disease and population variation. </li></ul></ul>

6.
Medical Informatics <ul><li>Medical informatics: </li></ul><ul><ul><li>Discipline of gathering, analyzing, and representing longitudinal patient studies in health and disease while providing decision support or predictive tools to assist in the diagnosis and prognosis of clinical patient care. </li></ul></ul>

7.
Cohort Studies <ul><li>Longitudinal study: </li></ul><ul><ul><li>Research study that involves repeated observations of the same items over long periods of time. </li></ul></ul><ul><li>Cohort: </li></ul><ul><ul><li>Group of subjects — most often humans from a given population — characterized by the experience of an event in a particular time span. </li></ul></ul>

10.
Areas of Applied Ontology <ul><li>Controlled vocabulary standards and management </li></ul><ul><li>Reporting and export of patient record content for analysis and aggregation </li></ul><ul><li>Population-based research </li></ul><ul><ul><li>Identification of cohorts </li></ul></ul>

12.
Patient Records <ul><li>Computer-based Patient Record: </li></ul><ul><ul><li>An electronic patient record that resides in a system specifically designed to support users through availability of complete and accurate data, practitioner reminders and alerts, clinical decision support systems, links to bodies of medical knowledge, and other aids. </li></ul></ul>

13.
Patient Records Cont. <ul><li>Longitudinal patient record: </li></ul><ul><ul><li>Patient records from different times, providers, and sites of care that are linked to form a lifelong view of a patient’s health care experience or a single patient record system with the same characteristics. </li></ul></ul>

14.
RDF Datasets <ul><ul><li>“ A SPARQL query is executed against an RDF Dataset which represents a collection of graphs. An RDF Dataset comprises one graph, the default graph, which does not have a name, and zero or more named graphs, where each named graph is identified by an IRI.“ </li></ul></ul>

15.
RDF Datasets Cont. <ul><li>Similar to a document collection in XPath 2.0 </li></ul><ul><li>The GRAPH operator can be used to scope query patterns to a particular graph or within all named graph </li></ul>

16.
SPARQL &Cohort Identification <ul><li>One named graph per patient record (a patient record graph ) </li></ul><ul><li>Each patient record graph is allocated a URI </li></ul><ul><li>No significant cross-graph statements. </li></ul><ul><ul><li>Beyond cohort identification, most processing happens within a single patient record graph </li></ul></ul>

17.
Use of Named Graphs <ul><li>In our vocabulary, there are instances of PatientRecord, Operation, Patient, etc. </li></ul><ul><li>PatientRecord resources share a URI with their containing graph </li></ul><ul><li>GRAPH operator can be used to optimize the search space </li></ul>

18.
Use of Named Graphs Cont. <ul><li>Easy to parallelize computation and optimal for cohort querying </li></ul><ul><ul><li>Constraints in the first part of query are cross-graph while the second part are intra-graph </li></ul></ul>

40.
Challenges <ul><li>Representing negation in SPARQL is painfully cumbersome </li></ul><ul><ul><li>Patients who had X but not Y </li></ul></ul><ul><li>No equivalent of SQL’s IN operator </li></ul><ul><ul><li>Find patients who had a diagnoses of an myocardial infarction, renal failure, or atrial fibrillation </li></ul></ul>